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Automatic assessment of disordered s...

Singh, Savyasachi.

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Automatic assessment of disordered speech intelligibility.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Automatic assessment of disordered speech intelligibility./
作者:	Singh, Savyasachi.
面頁冊數:	78 p.
附註:	Source: Dissertation Abstracts International, Volume: 76-08(E), Section: B.
Contained By:	Dissertation Abstracts International76-08B(E).
標題:	Electrical engineering. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3691372
ISBN:	9781321644890

Automatic assessment of disordered speech intelligibility.
Singh, Savyasachi.

Automatic assessment of disordered speech intelligibility. - 78 p.

Source: Dissertation Abstracts International, Volume: 76-08(E), Section: B.

Thesis (Ph.D.)--University of Florida, 2014.

This dissertation describes an automatic system for evaluation of the speech intelligibility for the patients of Parkinsons disease. The speech analysis system presented here is very flexible and versatile, and can also be used for dysarthric speech arising from other disease processes. The perceptual evaluation of disordered speech by a speech pathologist is an important way of identifying and differentiating among the different types of dysarthria, as well as quantifying the severity. Perceptual rating by listeners is called subjective evaluation of speech, whereas the proposed system is objective evaluation of speech which uses acoustic speech signal and automatic processing to predict scores that match human judgements. The proposed system is inexpensive, non-invasive, non time-consuming and highly consistent. Our system consists of two major steps and takes speech signal as input. We have developed a feature extraction system which employs a computational auditory model for obtaining spectro-temporal internal representations. The internal representation of the patients speech is compared to that of the healthy (perfectly intelligible) speech using correlation. The next step of processing is that of scoring which accepts feature vectors as inputs and maps them to a scalar value representing the speech intelligibility score. We solve the problem of scoring using the supervised learning technique of regression where feature vectors are the input variables (regressors) and quality score is the target (dependent) variable. We apply linear regression and other non-linear methods such as Gaussian process regression and support vector regression. These models are trained on a dataset with known perceptual ratings, and thereafter used for prediction. The prediction performance is evaluated using various metrics such as mean-square error and Pearson's correlation coefficient. In this document we study the performance of our system in the intelligibility prediction experiment using a database of 160 sentences collected from 48 Parkinsons patients. The results of intelligibility prediction are encouraging, for example, the correlation between perceptual scores and computed scores is high (> 0.9) showing excellent agreement.

ISBN: 9781321644890Subjects--Topical Terms:

649834
Electrical engineering.

Automatic assessment of disordered speech intelligibility.
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520 $a This dissertation describes an automatic system for evaluation of the speech intelligibility for the patients of Parkinsons disease. The speech analysis system presented here is very flexible and versatile, and can also be used for dysarthric speech arising from other disease processes. The perceptual evaluation of disordered speech by a speech pathologist is an important way of identifying and differentiating among the different types of dysarthria, as well as quantifying the severity. Perceptual rating by listeners is called subjective evaluation of speech, whereas the proposed system is objective evaluation of speech which uses acoustic speech signal and automatic processing to predict scores that match human judgements. The proposed system is inexpensive, non-invasive, non time-consuming and highly consistent. Our system consists of two major steps and takes speech signal as input. We have developed a feature extraction system which employs a computational auditory model for obtaining spectro-temporal internal representations. The internal representation of the patients speech is compared to that of the healthy (perfectly intelligible) speech using correlation. The next step of processing is that of scoring which accepts feature vectors as inputs and maps them to a scalar value representing the speech intelligibility score. We solve the problem of scoring using the supervised learning technique of regression where feature vectors are the input variables (regressors) and quality score is the target (dependent) variable. We apply linear regression and other non-linear methods such as Gaussian process regression and support vector regression. These models are trained on a dataset with known perceptual ratings, and thereafter used for prediction. The prediction performance is evaluated using various metrics such as mean-square error and Pearson's correlation coefficient. In this document we study the performance of our system in the intelligibility prediction experiment using a database of 160 sentences collected from 48 Parkinsons patients. The results of intelligibility prediction are encouraging, for example, the correlation between perceptual scores and computed scores is high (> 0.9) showing excellent agreement.
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